训练任务:检索和生成联合训练(unsupervised data) Prefix language modeling LM:一个由N个单词组成的块,并将这个块分成两个长度相等的子序列N/2。然后,输入第一个子序列,生成第二个子序列 检索:第一个子序列用作查询,第二个子序列对应于输出检索 Masked language modeling LM:15%的长度为3的token mask掉预测 检...
这篇文章提出的时候已经到了2021年的11月,杨植麟团队提出了一种新的预训练范式,Task-driven Language Modeling (TLM),根据下游需要完成的任务来有针对性的进行上游预训练数据的筛选。这种针对性的预训练可以大幅度加速下游的微调时间,从而取得更好的效果。 TLM的流程 这篇文章我问了很多人的观点,国内外的研究者的观...
a) Prefix language modeling 以N个字符为单位将文本分块,将每个块的文本切分为长度为N/2的两段子序列,用第一段子序列作为query,通过检索模块召回相关的文档,然后去生成结果,生成的目标是对应的第二段子序列。 b) Masked language modeling 以N个字符为单位将文本分块,对于每一个分块,随机抽样若干个平均长度为3...
Internet-augmented language models through few-shot prompting for open-domain question answering Angeliki Lazaridou, E. Gribovskaya, Wojciech Stokowiec, N. Grigorev 2022 Decoupled Context Processing for Context Augmented Language Modeling Zonglin Li, Ruiqi Guo, Surinder Ku...
Another important application of retrieval is its integration with language models, referred to as retrieval-augmented modeling. In this chapter, we will focus on this paradigm in detail and provide a taxonomy of retrieval-augmented modeling over multiple dimensions....
内容提示: arXiv:2002.08909v1 [cs.CL] 10 Feb 2020REALM: Retrieval-Augmented Language Model Pre-TrainingKelvin Guu * 1 Kenton Lee * 1 Zora Tung 1 Panupong Pasupat 1 Ming-Wei Chang 1AbstractLanguage model pre-training has been shown tocapture a surprising amount of world knowledge,crucial ...
This repository contains pre-trained models, corpora, indices, and code for pre-training, finetuning, retrieving and evaluating for the paperAtlas: Few-shot Learning with Retrieval Augmented Language Models Read ourAtlas blog postfor a quick overview of the project and how to run the code with ...
参考文献1.(2022,) Few-shot Learning with Retrieval Augmented Language Modelshttps://arxiv.org/pdf/2208.03299.pdf2.(2022,) Unsupervised dense information retrieval with contrastive learninghttps://arxiv.org/abs/2112.09118v4 本站仅提供存储服务,所有内容均由用户发布,如发现有害或侵权内容,请点击举报...
We introduce REPLUG, a retrieval-augmented language modeling framework that treats the language model (LM) as a black box and augments it with a tuneable retrieval model. Unlike prior retrieval-augmented LMs that train language models with special cross attention mechanisms to encode the retrieved ...
Retrieval-Augmented Generation(RAG)enhances the generative capacity of Large Language Models(LLMs)by appending retrieved documents to the current context.This approach has shown success in reading comprehension[1]and language modeling[2].RAG assumes the intent is in the input query,which ...